An agent is tasked with navigating to an object specified by its category label (e.g., chair) in an unexplored environment [1]}. At the start of an episode, the agent is spawned at a random navigable location in the environment. At each time-step \(t\) , the agent receives \(640 \times 480\)  RGB-D sensor readings \(s_t\) , \((x, y, \theta )\)  odometer readings, and the goal category \(o\) . The odometer readings are aggregated over time to obtain the agent's pose \(p_t\)  (relative to pose at \(t=0\) ). The agent then executes an action \(a_t \sim \mathcal {A}\) , where \(\mathcal {A}\)  consists of move_forward, turn_left, turn_right, and stop. The agent is required to navigate within \(d_s = 1.0{m}\)  of the object and execute stop to successfully complete the task. The episode terminates when the agent executes stop, or if it exceeds a time budget of \(T=500\)  steps.
